Delete app.py

app.py

@@ -1,505 +0,0 @@
-import os
-import time
-import uuid
-import asyncio
-import tempfile
-import shutil
-import threading
-import logging
-import torch
-import torchaudio
-from fastapi import FastAPI, Request, HTTPException, BackgroundTasks
-from fastapi.middleware.cors import CORSMiddleware
-from faster_whisper import WhisperModel
-from df.enhance import enhance, init_df, load_audio, save_audio
-from pyannote.audio import Pipeline as DiarizationPipeline
-from pipeline.postprocess import process
-
-
-class HealthAndStatusFilter(logging.Filter):
-    """جلوی چاپ لاگ‌های /status/ و /health رو می‌گیره تا کنسول شلوغ نشه (خودِ درخواست‌ها عادی پردازش میشن)"""
-    def filter(self, record):
-        message = record.getMessage()
-        return "/status/" not in message and "/health" not in message
-
-
-logging.getLogger("uvicorn.access").addFilter(HealthAndStatusFilter())
-
-torch.set_num_threads(2)  # جلوی رقابت بی‌فایده‌ی رشته‌ها روی CPU محدود رو می‌گیره
-
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-print("Loading Whisper model...", flush=True)
-model = WhisperModel("large-v3", device="cpu", compute_type="int8")
-print("Whisper model ready!")
-
-print("Loading DeepFilterNet model...", flush=True)
-df_model, df_state, _ = init_df()
-print("DeepFilterNet model ready!")
-
-USE_DEEPFILTERNET = False  # غیرفعال - شواهد نشون داد کمکی نمی‌کنه و بعضی صداها رو خراب می‌کنه
-
-print("Loading word-alignment model...", flush=True)
-align_bundle = torchaudio.pipelines.MMS_FA
-align_model = align_bundle.get_model().to("cpu")
-align_labels = align_bundle.get_labels()
-align_dictionary = {c: i for i, c in enumerate(align_labels)}
-print("Word-alignment model ready!")
-
-print("Loading speaker-diarization model...", flush=True)
-HF_TOKEN = os.environ.get("HF_TOKEN")
-diarize_pipeline = DiarizationPipeline.from_pretrained(
-    "pyannote/speaker-diarization-3.1", use_auth_token=HF_TOKEN
-)
-print("Speaker-diarization model ready!")
-
-MIN_NOISE_BLOCK_SEC = 1.0
-JOB_TTL_SECONDS = 3 * 60 * 60  # کارهای قدیمی بعد از ۳ ساعت پاک میشن (برای فایل‌های سنگین که ممکنه پردازششون طولانی بشه)
-STAGE_TIMEOUT_SEC = 1200  # سقف زمانی برای هر مرحله‌ی سنگین (۲۰ دقیقه - فقط محافظ آخر در برابر گیرکردن واقعی)
-
-# ─── نگهداری وضعیت کارها (Jobs) توی حافظه ───────────────────────────────────
-jobs = {}
-jobs_lock = threading.Lock()
-
-
-def new_job(single_speaker_mode=False):
-    job_id = uuid.uuid4().hex
-    tmp_dir = tempfile.mkdtemp(prefix=f"subflow_{job_id}_")
-    with jobs_lock:
-        jobs[job_id] = {
-            "status": "uploading",
-            "detail": "در انتظار دریافت فایل...",
-            "created_at": time.time(),
-            "tmp_dir": tmp_dir,
-            "chunk_paths": [],
-            "srt": None,
-            "language": None,
-            "duration": None,
-            "error": None,
-            "single_speaker_mode": single_speaker_mode,
-        }
-    return job_id
-
-
-def get_job(job_id):
-    with jobs_lock:
-        job = jobs.get(job_id)
-    if job is None:
-        raise HTTPException(status_code=404, detail="این کار پیدا نشد یا منقضی شده")
-    return job
-
-
-def update_job(job_id, **kwargs):
-    with jobs_lock:
-        if job_id in jobs:
-            jobs[job_id].update(kwargs)
-
-
-def cleanup_expired_jobs():
-    now = time.time()
-    with jobs_lock:
-        expired = [jid for jid, j in jobs.items() if now - j["created_at"] > JOB_TTL_SECONDS]
-    for jid in expired:
-        with jobs_lock:
-            job = jobs.pop(jid, None)
-        if job and os.path.isdir(job["tmp_dir"]):
-            shutil.rmtree(job["tmp_dir"], ignore_errors=True)
-        print(f"[cleanup] removed expired job {jid}")
-
-
-# ─── منطق تشخیص گوینده و هم‌ترازی ────────────────────────────────────────────
-
-def build_speaker_lookup(diarization):
-    """
-    لیست بازه‌های گوینده‌ها رو یه‌بار مرتب می‌کنه تا جست‌وجوی «این لحظه مال کدوم
-    گوینده‌ست» برای هر جمله، به‌جای گشتن کامل لیست، سریع (با جست‌وجوی دودویی) انجام بشه.
-    """
-    turns = sorted(
-        [(turn.start, turn.end, speaker) for turn, _, speaker in diarization.itertracks(yield_label=True)],
-        key=lambda x: x[0],
-    )
-    return turns
-
-
-def get_speaker_at(turns, time_sec):
-    import bisect
-    starts = [t[0] for t in turns]
-    idx = bisect.bisect_right(starts, time_sec) - 1
-    if idx >= 0:
-        start, end, speaker = turns[idx]
-        if start <= time_sec <= end:
-            return speaker
-    return None
-
-
-def get_speaker_for_segment(turns, seg_start, seg_end):
-    """
-    به‌جای اعتماد به یه لحظه‌ی تنها، چند نقطه در طول جمله رو چک می‌کنه
-    و گوینده‌ای که بیشترین رأی رو آورده برمی‌گردونه.
-    """
-    duration = seg_end - seg_start
-    if duration <= 0:
-        return get_speaker_at(turns, seg_start)
-
-    sample_points = [seg_start + duration * frac for frac in (0.2, 0.4, 0.5, 0.6, 0.8)]
-    votes = {}
-    for t in sample_points:
-        speaker = get_speaker_at(turns, t)
-        if speaker is not None:
-            votes[speaker] = votes.get(speaker, 0) + 1
-
-    if not votes:
-        return None
-    return max(votes, key=votes.get)
-
-
-def find_leading_noise_cutoff(turns, whisper_start, search_back_sec=4.0):
-    STABLE_SPEAKER_SEC = 0.2
-    window_start = max(0, whisper_start - search_back_sec)
-    step = 0.05
-    t = whisper_start
-    noise_end = None
-    noise_start = None
-    speaker_run_start = None
-
-    while t > window_start:
-        speaker = get_speaker_at(turns, t)
-
-        if speaker is None:
-            speaker_run_start = None
-            if noise_end is None:
-                noise_end = t
-            noise_start = t
-        else:
-            if speaker_run_start is None:
-                speaker_run_start = t
-            run_len = speaker_run_start - t
-            if run_len >= STABLE_SPEAKER_SEC:
-                break
-            if noise_end is None:
-                noise_end = t
-            noise_start = t
-
-        t -= step
-
-    if noise_start is not None and noise_end is not None:
-        block_len = noise_end - noise_start
-        if block_len >= MIN_NOISE_BLOCK_SEC:
-            return noise_end
-    return None
-
-
-def realign_word_timestamps(waveform_16k, words, whisper_start, whisper_end, cutoff_start=None):
-    try:
-        margin = 0.5
-        raw_start = whisper_start - margin
-        effective_start = max(raw_start, cutoff_start) if cutoff_start is not None else raw_start
-
-        start_sample = max(0, int(effective_start * 16000))
-        end_sample = int((whisper_end + margin) * 16000)
-        clip = waveform_16k[:, start_sample:end_sample]
-
-        clean_words = []
-        for w in words:
-            token = "".join(ch for ch in w["word"].lower() if ch in align_dictionary)
-            if token:
-                clean_words.append((w, token))
-
-        if not clean_words:
-            return words
-
-        transcript = "".join(tok for _, tok in clean_words)
-        tokens = [align_dictionary[c] for c in transcript]
-
-        with torch.inference_mode():
-            emission, _ = align_model(clip)
-
-        targets = torch.tensor([tokens], dtype=torch.int32)
-        input_lengths = torch.tensor([emission.shape[1]])
-        target_lengths = torch.tensor([len(tokens)])
-
-        aligned_tokens, scores = torchaudio.functional.forced_align(
-            emission, targets, input_lengths, target_lengths, blank=0
-        )
-        spans = torchaudio.functional.merge_tokens(aligned_tokens[0], scores[0])
-
-        ratio = clip.shape[1] / emission.shape[1] / 16000
-        new_words = []
-        span_idx = 0
-        for w, tok in clean_words:
-            n_chars = len(tok)
-            matched = [s for s in spans[span_idx:] if s.token != 0][:n_chars]
-            if len(matched) < n_chars:
-                new_words.append(w)
-                continue
-            w_start = matched[0].start * ratio + effective_start
-            w_end = matched[-1].end * ratio + effective_start
-            new_words.append({"word": w["word"], "start": round(max(0, w_start), 3), "end": round(max(0, w_end), 3)})
-            span_idx += n_chars
-
-        return new_words if len(new_words) == len(words) else words
-    except Exception as e:
-        print(f"  [align] skipped due to: {type(e).__name__}: {e}")
-        return words
-
-
-def run_transcription(audio_path):
-    """
-    نکته‌ی مهم: model.transcribe فوراً برنمی‌گرده - یه نتیجه‌ی "تنبل" میده که کار
-    واقعیش رو دیر انجام میده. برای اینکه کل زمان واقعی پردازش زیر نظارت سقف زمانی و
-    پیام‌های زنده باشه، همینجا مجبورش می‌کنیم کارش رو کامل انجام بده (list کردن).
-    """
-    segments_raw, info = model.transcribe(
-        audio_path,
-        language="en",
-        word_timestamps=True,
-        vad_filter=True,
-        vad_parameters=dict(
-            min_silence_duration_ms=300,
-            speech_pad_ms=200,
-            threshold=0.3,
-        ),
-    )
-    segments_list = list(segments_raw)
-    return segments_list, info
-
-
-class SingleSpeakerResult:
-    """
-    یه نتیجه‌ی «قلابی» که همون شکل خروجی diarize_pipeline رو تقلید می‌کنه (متد itertracks داره)
-    ولی هیچ پردازش واقعی‌ای روی کل فایل انجام نداده - فقط می‌گه «همه‌جا همون یه گوینده‌ست».
-    فقط وقتی استفاده میشه که خودِ کاربر صراحتاً بگه فایل تک‌گوینده‌ست.
-    """
-    def __init__(self, duration):
-        self.duration = duration
-
-    def itertracks(self, yield_label=False):
-        class FakeTurn:
-            def __init__(self, start, end):
-                self.start = start
-                self.end = end
-        yield FakeTurn(0, self.duration), None, "SPEAKER_00"
-
-
-def run_diarization(audio_path, single_speaker_mode=False, job_id=None):
-    if single_speaker_mode:
-        print("  [diarization] single-speaker mode selected by user — skipping full diarization entirely")
-        info = torchaudio.info(audio_path)
-        duration = info.num_frames / info.sample_rate
-        return SingleSpeakerResult(duration)
-
-    return diarize_pipeline(audio_path, max_speakers=8)
-
-
-def run_alignment_pass(segments_raw, align_waveform, diarization):
-    turns = build_speaker_lookup(diarization)
-    segments = []
-    for seg in segments_raw:
-        words = list(seg.words)
-        if not words:
-            continue
-        word_dicts = [{"word": w.word, "start": w.start, "end": w.end} for w in words]
-
-        cutoff = find_leading_noise_cutoff(turns, word_dicts[0]["start"])
-        if cutoff is not None:
-            print(f"  [noise-trim] '{seg.text.strip()[:40]}...' cutoff moved from {word_dicts[0]['start']:.2f}s to {cutoff:.2f}s")
-
-        realigned = realign_word_timestamps(
-            align_waveform, word_dicts, word_dicts[0]["start"], word_dicts[-1]["end"], cutoff_start=cutoff
-        )
-
-        speaker = get_speaker_for_segment(turns, realigned[0]["start"], realigned[-1]["end"])
-
-        segments.append({
-            "text": seg.text.strip(),
-            "start": realigned[0]["start"],
-            "end": realigned[-1]["end"],
-            "words": realigned,
-            "speaker": speaker,
-        })
-    return segments
-
-
-class ProcessingTimeout(Exception):
-    pass
-
-
-def run_blocking_with_timeout(job_id, blocking_fn, *args, status, detail, max_seconds=STAGE_TIMEOUT_SEC):
-    """
-    یه تابع سنگین و بلاک‌کننده رو توی یه thread جدا اجرا می‌کنه، و تا وقتی تموم بشه،
-    هر چند ثانیه یه پیام "هنوز کار می‌کنم" روی همون status می‌فرسته - این هم جلوی
-    قطع‌شدن اتصال به‌خاطر سکوت طولانی رو می‌گیره، هم UI رو زنده نگه می‌داره.
-    اگه از max_seconds بیشتر طول بکشه، به‌جای قفل ابدی، خطای مشخص میده.
-    """
-    result_holder = {}
-    error_holder = {}
-
-    def runner():
-        try:
-            result_holder["value"] = blocking_fn(*args)
-        except Exception as e:
-            error_holder["error"] = e
-
-    update_job(job_id, status=status, detail=detail)
-    t = threading.Thread(target=runner, daemon=True)
-    t.start()
-
-    elapsed = 0
-    while t.is_alive():
-        t.join(timeout=3)
-        if t.is_alive():
-            elapsed += 3
-            if elapsed >= max_seconds:
-                raise ProcessingTimeout(f"مرحله‌ی «{detail}» بیشتر از {max_seconds} ثانیه طول کشید و متوقف شد.")
-            update_job(job_id, status=status, detail=f"{detail} ({elapsed} ثانیه)")
-
-    if "error" in error_holder:
-        raise error_holder["error"]
-    return result_holder.get("value")
-
-
-def process_job(job_id):
-    """کل پایپلاین پردازش - این تابع توی یه thread پس‌زمینه اجرا میشه، نه توی درخواست HTTP."""
-    job = get_job(job_id)
-    tmp_dir = job["tmp_dir"]
-    single_speaker_mode = job.get("single_speaker_mode", False)
-    tmp_path = os.path.join(tmp_dir, "input.wav")
-    enhanced_path = None
-
-    try:
-        with open(tmp_path, "wb") as f:
-            for chunk_path in job["chunk_paths"]:
-                with open(chunk_path, "rb") as c:
-                    f.write(c.read())
-        print(f"Job {job_id}: assembled {len(job['chunk_paths'])} chunks into {tmp_path}")
-
-        audio_to_transcribe = tmp_path
-
-        if USE_DEEPFILTERNET:
-            update_job(job_id, status="cleaning", detail="در حال حذف نویز پس‌زمینه")
-            audio_data, _ = load_audio(tmp_path, sr=df_state.sr())
-            enhanced_audio = enhance(df_model, df_state, audio_data, atten_lim_db=20)
-            enhanced_path = os.path.join(tmp_dir, "enhanced.wav")
-            save_audio(enhanced_path, enhanced_audio, df_state.sr())
-            audio_to_transcribe = enhanced_path
-
-        segments_raw_list, info = run_blocking_with_timeout(
-            job_id, run_transcription, audio_to_transcribe,
-            status="transcribing", detail="مدل large-v3 در حال شنیدن صداست",
-        )
-        update_job(job_id, detail=f"{len(segments_raw_list)} جمله تشخیص داده شد")
-
-        align_waveform, sr = torchaudio.load(audio_to_transcribe)
-        if sr != 16000:
-            align_waveform = torchaudio.functional.resample(align_waveform, sr, 16000)
-        if align_waveform.shape[0] > 1:
-            align_waveform = align_waveform.mean(dim=0, keepdim=True)
-
-        if single_speaker_mode:
-            diarization = run_diarization(audio_to_transcribe, single_speaker_mode=True)
-            update_job(job_id, status="aligning", detail="تشخیص گوینده رد شد (حالت تک‌گوینده)")
-        else:
-            diarization = run_blocking_with_timeout(
-                job_id, run_diarization, audio_to_transcribe, False, job_id,
-                status="aligning", detail="در حال تشخیص گوینده‌ها",
-            )
-        update_job(job_id, detail="گوینده‌ها شناسایی شدند")
-
-        segments = run_blocking_with_timeout(
-            job_id, run_alignment_pass, segments_raw_list, align_waveform, diarization,
-            status="aligning", detail=f"در حال هماهنگ‌سازی زمان‌بندی {len(segments_raw_list)} جمله",
-        )
-        update_job(job_id, detail=f"{len(segments)} خط شناسایی شد")
-
-        print(f"Job {job_id}: got {len(segments)} segments")
-        for i, seg in enumerate(segments):
-            print(f"  seg[{i}]: '{seg['text']}' | speaker={seg['speaker']} | dur={seg['end']-seg['start']:.2f}s")
-
-        update_job(job_id, status="processing", detail=f"در حال چیدمان {len(segments)} خط زیرنویس")
-        srt_content = process(segments)
-        update_job(job_id, detail="زیرنویس ساخته شد")
-
-        update_job(
-            job_id,
-            status="done",
-            detail="زیرنویس آماده شد",
-            srt=srt_content,
-            language=info.language,
-            duration=info.duration,
-        )
-        print(f"Job {job_id}: done!")
-
-    except ProcessingTimeout as e:
-        print(f"Job {job_id}: TIMEOUT: {e}")
-        update_job(job_id, status="error", error=str(e))
-    except Exception as e:
-        print(f"Job {job_id}: ERROR: {type(e).__name__}: {e}")
-        import traceback
-        traceback.print_exc()
-        update_job(job_id, status="error", error=str(e))
-    finally:
-        pass  # پاک‌سازی جداگانه و فقط موقع شروع کارهای جدید انجام میشه، نه بلافاصله بعد از اتمام همین کار
-
-
-@app.get("/health")
-async def health():
-    return {"status": "ok", "message": "SubFlow backend is running"}
-
-
-@app.post("/upload/start")
-async def upload_start(request: Request):
-    cleanup_expired_jobs()
-    body = {}
-    try:
-        body = await request.json()
-    except Exception:
-        pass
-    single_speaker_mode = bool(body.get("single_speaker_mode", False))
-    job_id = new_job(single_speaker_mode)
-    return {"job_id": job_id}
-
-
-@app.post("/upload/chunk/{job_id}")
-async def upload_chunk(job_id: str, request: Request):
-    job = get_job(job_id)
-    chunk_bytes = await request.body()
-    chunk_index = len(job["chunk_paths"])
-    chunk_path = os.path.join(job["tmp_dir"], f"chunk_{chunk_index:06d}.bin")
-    with open(chunk_path, "wb") as f:
-        f.write(chunk_bytes)
-    with jobs_lock:
-        jobs[job_id]["chunk_paths"].append(chunk_path)
-    return {"received": chunk_index}
-
-
-@app.post("/upload/finish/{job_id}")
-async def upload_finish(job_id: str, background_tasks: BackgroundTasks):
-    job = get_job(job_id)
-    total_bytes = sum(os.path.getsize(p) for p in job["chunk_paths"])
-    print(f"Job {job_id}: upload finished, {len(job['chunk_paths'])} chunks, {total_bytes} bytes total")
-    update_job(job_id, status="receiving", detail="فایل آماده‌ی پردازش شد")
-    background_tasks.add_task(process_job, job_id)
-    return {"status": "processing_started"}
-
-
-@app.get("/status/{job_id}")
-async def status(job_id: str):
-    job = get_job(job_id)
-    response = {
-        "status": job["status"],
-        "detail": job["detail"],
-    }
-    if job["status"] == "done":
-        response["srt"] = job["srt"]
-        response["language"] = job["language"]
-        response["duration"] = job["duration"]
-    elif job["status"] == "error":
-        response["message"] = job["error"]
-    return response